Section 1: Data Error Checks


1. Project Coverage

In [29]:
print("Districts: ", raw_data["District"].value_counts().shape[0], 
      "  |  Blocks: ", raw_data["Block"].value_counts().shape[0],
      "  |  Gram Panchayats: ", raw_data["GP ID"].value_counts().shape[0],
      "  |  Villages: ", raw_data["Village ID"].value_counts().shape[0],
      "  |  Surveys: ", raw_data["Village"].count())
Districts:  10   |  Blocks:  63   |  Gram Panchayats:  239   |  Villages:  625   |  Surveys:  43183
In [31]:
display_barh(raw_data['District'].value_counts(),title="Number of Surveys in Each District", size=[6,6])

In [32]:
hh_no_cnsnt = raw_data[raw_data["I have consent from family head/ adult member to proceed with the survey."] == "No"]
hh_no_cnsnt.shape[0]
Out[32]:
1056

3. Total Duplicate records:</font>

Based on the unique combination - District + Block + Gram Panchayat + Village + Household Number
In [33]:
### Data Error Condition 1: Number of Duplicate Records identified using Duplicate Key above
dup_records = raw_data[raw_data["is_duplicate_record"] == True]
dup_records.shape[0]
Out[33]:
585

Who Submitted the Duplicate Records-

In [34]:
dup_records['Volunteer Name'].value_counts()
Out[34]:
Sarita Majhi            52
Manasi Raita            44
Dillip Kumar Sabar      40
Sudam Sabar             36
Manaya Raita            29
Urbashi Rout            26
Asish Kumar Sabar       21
Gayatri Sabar           20
Puspanjali Bishoi       18
Chandra Singh Majhi     18
Laxmi Karjee            17
Rajani Karjee           16
Puspanjali Bhuyan       15
Gouri Karjee            15
Premalata Gamango       13
Eliya Sabar             13
Sujata Bhuyan           13
Dillip Raika            12
Sumanta Raita           12
Pabitra Naik            11
Dambrudhar Dalai        10
Baman Sing Majhi         9
Dilip Kumar Nayak        9
Brundabati Sabar         8
DukhiRam Naik            8
Naman Bhuyan             7
Sunthani Gamango         7
Prasant Kumar Nayak      7
Abnaijar Raika           7
Prabhat Raita            7
Suniel Sabar             6
Ajaya Patra              6
Rajeshwari Badaraita     6
Subhasmita Biswal        6
Jeebita Bhuyan           6
Kulamani Majhi           5
Astina Raika             4
Bibhuti Patra            4
Bairagi Karjee           3
Gouri Shankar Sabar      3
Bidyutprava Praharaj     2
Jihosay Mandal           2
Sonu Nayak               2
Debashis Nayak           2
Padmanabham Sabar        1
Priya Ranjan Pradhan     1
Aswini Kumar Naik        1
Niraj Nial               1
Rabi Badaraita           1
Ebel Raita               1
Mahendra Kumar Naik      1
Ranjit Karjee            1
Name: Volunteer Name, dtype: int64

Removing the duplicate records from analysis...

Number of unique records:

In [35]:
raw_data.drop_duplicates(subset=("District",
                                         "Block",
                                         "Gram Panchayat",
                                         "Village",
                                         "Household number"),
                                  keep='last',
                                  inplace=True)
raw_data.shape[0]
Out[35]:
42598

4. Number of records without consent*

In [36]:
no_cnsnt_col="I have consent from family head/ adult member to proceed with the survey."
hh_no_cnsnt = raw_data[raw_data[no_cnsnt_col] == "No"]
hh_no_cnsnt.shape[0]
Out[36]:
1033
In [37]:
display_bar(hh_no_cnsnt["District"].value_counts(),title="Number of Households not Surveyed", size=[14,3])
In [38]:
raw_data["Why did you not get permission to do the survey?"].value_counts()
Out[38]:
House locked - family away for few days          575
House locked - family does not live there now    399
Respondent declined                               37
No adult at home                                  22
Name: Why did you not get permission to do the survey?, dtype: int64
In [39]:
display_donut(raw_data["Why did you not get permission to do the survey?"],
              title='Why did you not get permission to do the survey?',
             width=5,
             height=5,
             pct=True)

List of Villages with number of households where survey could not be conducted-

In [42]:
create_download_link(hh_no_cnsnt_pvt,
                     title='Click to Download',
                     filename='List of Households - Survey not conducted.csv',
                    level='Village')

Removing the records without consent from analysis

Number of records with consent-

In [43]:
#raw_data=raw_data.drop(raw_data[raw_data["I have consent from family head/ adult member to proceed with the survey."] == "No"].index)
value.append('Total Households')
print(value)
orig_hh_cnt=raw_data.pivot_table(index=['District','Block','Gram Panchayat','Village'],
                                values=value,
                                aggfunc=np.sum) #Create Copy of Original Household Count
raw_data=raw_data[raw_data[no_cnsnt_col]=="Yes"] #Keep records only with consent
raw_data.shape[0]
['Why did you not get permission to do the survey?-House locked - family away for few days', 'Why did you not get permission to do the survey?-House locked - family does not live there now', 'Why did you not get permission to do the survey?-Respondent declined', 'Why did you not get permission to do the survey?-No adult at home', 'Total Households']
Out[43]:
41565

5. Missing Data in Mandatory Columns:

Observations:
-----
Missing Data in  Caste (Avoiding asking. Ask only if doubtful) :  251 ( 0.6 %)
.....
ST         22164
OBC        12297
SC          3576
General     3277
Name: Caste (Avoiding asking. Ask only if doubtful), dtype: int64
-----

Section 2: Data Analysis

1. Overview

Total Households included in Survey:

In [45]:
total_hh = raw_data.shape[0]
print(total_hh)
41565

Households by Social Category:

Out[46]:
Value %
ST 22164 53.65
OBC 12297 29.76
SC 3576 8.66
General 3277 7.93

Distribution of Households across different socio-economic categories in district

In [48]:
raw_data.pivot_table(index=["District"], 
                        columns=["Caste (Avoiding asking. Ask only if doubtful)"],
                        values=['Total Households'],
                        aggfunc={'Total Households':[np.sum]}).style.apply(highlight_max,axis=1)
Out[48]:
Total Households
sum
Caste (Avoiding asking. Ask only if doubtful) OBC General ST SC
District
GAJAPATI 434 7 8207 60
GANJAM 6370 2703 2518 1572
JHARSUGUDA 676 85 1072 180
KALAHANDI 500 26 3703 1026
KANDHAMAL 5 4 507 96
KEONJHAR 2531 239 1811 345
MAYURBHANJ 891 202 1007 120
NAYAGARH 804 6 513 34
RAYAGADA 1 1 1423 30
SUNDERGARH 85 4 1403 113

2. Availability of TBR

Out[49]:
Total Households % of Total
Availability of TBR
Yes, I have TBR 31093 74.81
Toilet only 1609 3.87
No, never had TBR 7229 17.39
Had TBR but not now 1634 3.93

Distribution of Villages by % of Households having TBR

No of Villages with 100% households having TBR or Toilet:  20  ( 3.2 %)
No of Villages with No(0%) households having TBR or Toilet:  4  ( 0.64 %)

Why there was never a toilet in household?

In [58]:
print("Households which never had a toilet:",hh_by_tbr.loc['No, never had TBR','Total Households'] )
display_donut(raw_data['Why did you never have a Toilet?'],
             title='',
             width=6,
             height=6,
             pct=True)
Households which never had a toilet: 7229

When TBR is now not available in household, but was there earlier, what happened to it?

In [59]:
display_barh(raw_data['What happened to your TBR?'].value_counts(),
              title='Current Status of TBR',
           size=[7,4])

So what is the common practice for defecation in households?

In [60]:
display_donut(raw_data['You do not have a Toilet. Where do you defecate?'],
              title='', 
              width=4, 
              height=4,
             pct=True)

Which are the villages where open defecation is widespread (Top 20) ?

In [61]:
col='You do not have a Toilet. Where do you defecate?'
In [63]:
od_hh_dist=raw_data.pivot_table(index=['District','Block','Gram Panchayat','Village'],
                    values=value,
                    aggfunc=np.sum)
od_hh_dist.fillna(0,inplace=True)
In [64]:
od_hh_dist.sort_values(value[0], ascending=False).head(20)
Out[64]:
Total Households You do not have a Toilet. Where do you defecate?-Open Defecation You do not have a Toilet. Where do you defecate?-Other (please specify) You do not have a Toilet. Where do you defecate?-Other's Toilet
District Block Gram Panchayat Village
KALAHANDI THUAMUL RAMPUR KERPAI MAJHIGAON 90 77 0 0
KEONJHAR KEONJHAR HANDIBHANGA KAINSI 262 70 0 2
MAYURBHANJ JASHIPUR BAUNSANALI BANDIRAPOSI 181 68 0 3
KALAHANDI THUAMUL RAMPUR KANIGUMA KARANG 90 66 0 0
GAJAPATI MOHANA BIRIKOTE MAHAPADAR 80 65 0 0
GANJAM PURUSOTTAMPUR TANKACHHAI JHADABANDHA-A 266 60 0 9
KALAHANDI THUAMUL RAMPUR KERPAI KACHALEKHA 80 53 0 3
GANJAM JAGANATH PRASAD RUDHAPADAR SARANGPATA 103 52 0 0
SUNDERGARH KUARMUNDA PADAMPUR JAGADISHPUR 184 52 0 10
KALAHANDI THUAMUL RAMPUR KANIGUMA JABANG 96 51 0 0
MAYURBHANJ SUKURLI GALUSAHI GALUSAHI-B 151 51 0 2
RARUAN ANGARPADA ANGARPADA 233 50 0 9
NAYAGARH GANIA KISHORE PRASAD PATURIA 177 50 0 2
KALAHANDI THUAMUL RAMPUR GOPALPUR KUMUDABAHAL 113 49 0 0
GAJAPATI MOHANA JUBA JUBA 95 49 0 0
KALAHANDI THUAMUL RAMPUR THUAMUL RAMPUR KOSABARA 139 48 0 0
JHARSUGUDA JHARSUGUDA RAJPUR AMBADARHA 133 48 0 1
KALAHANDI THUAMUL RAMPUR NAKRUNDI CHULBADI 78 47 0 0
BADA CHHATRANG SUKSAN 47 46 0 1
MAYURBHANJ SUKURLI JAMUTI JAMUTY 140 46 0 1

All Villages where ODF is in practice-

In [65]:
create_download_link(od_hh_dist,filename="Distribution of OD Households.csv", level='Village')

3. Water Supply to Households

Households Connected to Water Suppy

Out[66]:
Total Households % of Total
Household Connected with Water Supply System
Yes, Having Water Connection 31016 74.620000
Never had water connection 9976 24.000000
Had Water Connection in past but not now 573 1.380000

Observations: 25.38% of the households do not have water connection. Out of those 94.57% (i.e. 24% of total) never had the connection


Distribution of Villages by % of Households Not having Water Suppply Connection

No. of Villages with 100% households having Water Supply Connection:  21  ( 3.36 %)
No. of Villages with No (0%) households having Water Supply Connection:  6  ( 0.96 %)

Why did households never have tap connection?

Distribution of households (by %) by major reasons for not getting water supply connection:

In [74]:
print("Total Households which never connected to WSS: ",hh_by_ws.iloc[1,0],"(",hh_by_ws.iloc[1,1],"%)")
Total Households which never connected to WSS:  9976 ( 24.0 %)

a. Across all regions -

In [77]:
value = [col+"-"+x for x in options]
hh_nvr_cnctd_sw = raw_data[raw_data["Did you ever have tap connection (individual pipeline) to your house/ TBR?"]=="No"].pivot_table(index='State',
               values = value,
               aggfunc=np.mean).round(2)
display_bar(hh_nvr_cnctd_sw[value],size=[15,9],title='')

b. In districts

In [78]:
hh_nvr_cnctd_dw = raw_data[raw_data["Did you ever have tap connection (individual pipeline) to your house/ TBR?"]=="No"].pivot_table(index='District',
               values = value,
               aggfunc=np.mean).round(2)

display_bar(hh_nvr_cnctd_dw[value],size=[15,10],title='')

c. In Villages

In [79]:
### Create a Pivot % of such households not receiving water village wise along with reason 
hh_nvr_cnctd_vw = raw_data.pivot_table(index = ['District','Block','Gram Panchayat', 'Village'],
                    values = value,
                    aggfunc = np.mean
                    ).round(2)

create_download_link(hh_nvr_cnctd_vw,title="Download Complete Table",filename="Reasons for No WSS Connection.csv", level='Village')

What is the willingness of households to reconnect the water supply?

Among those households which had a tap connection earlier but not at present

In [80]:
print("Total Households which wever once connected to WSS but not now: ",hh_by_ws.iloc[2,0],"(",hh_by_ws.iloc[2,1],"%)")
Total Households which wever once connected to WSS but not now:  573 ( 1.38 %)

*Among those households which never had a tap connection***

In [83]:
print("Total Households which never connected to WSS: ",hh_by_ws.iloc[1,0],"(",hh_by_ws.iloc[1,1],"%)")
Total Households which never connected to WSS:  9976 ( 24.0 %)

Condition of tap connection (pipeline to house/ TBR)

*Observations: 9.26% of the household tap connections are not working*


Supply of water to households through pipeline

Total Households with Water Supply Connection:  31016
Out[87]:
Water supplied to Households % of Households (with Water Supply Connection) % of Total Households
Yes 26883 86.67 64.68
No 4133 13.33 9.94

How often do the households get the water? (Number of households with get water regularlrly)


Overall Status of Water Supply System in the Village

Out[89]:
Status of Water Supply Total Households % of Total
0 365 days a year but not 24/7 12466 29.990000
1 365 days a year and 24/7 7506 18.060000
2 Not 12 months but 24/7 5381 12.950000
3 Not 12 months and not 24/7 1530 3.680000
4 Never get water supply 4133 9.940000
5 Never had water connection 9976 24.000000
6 Had Water Connection in past but not now 573 1.380000

Distribution of villages by frequency of water supply to households (%)

Out[92]:
<matplotlib.axes._subplots.AxesSubplot at 0x15ef781e3c8>

How often the given % of connected households get the water supply in the village?

Out[93]:
(-0.001, 5.0] (5.0, 10.0] (10.0, 20.0] (20.0, 30.0] (30.0, 40.0] (40.0, 50.0] (50.0, 60.0] (60.0, 70.0] (70.0, 80.0] (80.0, 90.0] (90.0, 100.0]
How often do you get water?-365 days a year but not 24/7-Binned 347 1 1 2 4 2 1 4 1 5 173
How often do you get water?-Not 12 months and not 24/7-Binned 505 1 1 3 2 3 2 2 2 0 20
How often do you get water?-Not 12 months but 24/7-Binned 382 2 4 1 2 2 2 2 3 2 139
How often do you get water?-365 days a year and 24/7-Binned 347 6 2 0 4 1 0 3 1 2 175
Number of Villages having 100% of Households receiving Water-365 days a year but not 24/7:  154  ( 24.64 %)
Number of Villages having No (0%) Household receiving Water-365 days a year but not 24/7:  344  ( 55.04 %)
Number of Villages having 100% of Households receiving Water-Not 12 months and not 24/7:  15  ( 2.4 %)
Number of Villages having No (0%) Household receiving Water-Not 12 months and not 24/7:  502  ( 80.32 %)
Number of Villages having 100% of Households receiving Water-Not 12 months but 24/7:  138  ( 22.08 %)
Number of Villages having No (0%) Household receiving Water-Not 12 months but 24/7:  375  ( 60.0 %)
Number of Villages having 100% of Households receiving Water-365 days a year and 24/7:  174  ( 27.84 %)
Number of Villages having No (0%) Household receiving Water-365 days a year and 24/7:  333  ( 53.28 %)
In [95]:
create_download_link(df=wss_connection_status,
                     filename='List of Villages - % of Households by Frequency of Water Supply.csv',
                     title='Download Complete List',
                     level='Village')

Water shortage month-wise (% of households having a water supply which do not get water in each month)

In [96]:
print("Total Households which get water supply: ",raw_data[raw_data['Do you have water supply to your house/TBR ?']=='Yes'].shape[0])
Total Households which get water supply:  26883
Out[99]:
How often do you get water? 365 days a year and 24/7 365 days a year but not 24/7 Not 12 months and not 24/7 Not 12 months but 24/7
Which months do you not have water supply?-January 0.0 0.0 0.00 0.00
Which months do you not have water supply?-February 0.0 0.0 14.58 3.68
Which months do you not have water supply?-March 0.0 0.0 70.65 57.18
Which months do you not have water supply?-April 0.0 0.0 96.86 94.65
Which months do you not have water supply?-May 0.0 0.0 93.27 94.74
Which months do you not have water supply?-June 0.0 0.0 60.72 56.27
Which months do you not have water supply?-July 0.0 0.0 9.48 7.99
Which months do you not have water supply?-August 0.0 0.0 4.90 1.47
Which months do you not have water supply?-September 0.0 0.0 3.92 1.93
Which months do you not have water supply?-October 0.0 0.0 2.75 1.38
Which months do you not have water supply?-November 0.0 0.0 2.75 0.04
Which months do you not have water supply?-December 0.0 0.0 2.94 0.02

Why do households do not get 24x7 water supply?


Does the entire village get water supply?

Total Households With Water Supply:
Out[101]:
Total Households    31016.00
% of Total             74.62
Name: Yes, Having Water Connection, dtype: float64

Observation: a. Villages where more than 20% of the connected households do not get water:

128

b. Villages where over 50% of connected households do not get water:

91

c. Village where 100% of households receive water:

309

d. Village where no (0%) household receive water:

78
In [112]:
create_download_link(df=villages_with_no_supply,title="Click to Download",
                     filename='List of Villages-% of HH with Water Supply.csv',
                    level='Village')
Out[112]:

Why is the water not being supplied?

Distribution of hoseholds(%) by major reasons for no supply of water:


a. Across all regions -


b. In districts


c. In Villages (Top 50)

Cells in red denote the village where the issue is most common

% of Households with No Water Supply Reason(s) for no water supply-Caste/ Social Issue Reason(s) for no water supply-Committee decided Reason(s) for no water supply-Elevation issue Reason(s) for no water supply-Low/No pressure in the individual pipeline Reason(s) for no water supply-Non-payment of user fee Reason(s) for no water supply-Other Reason(s) for no water supply-Problem with the distribution pipeline Reason(s) for no water supply-Problem with the individual pipeline Reason(s) for no water supply-Water shortage
District Block Gram Panchayat Village
GAJAPATI GOSANI SABARA PADMAPUR 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 100.000000
KALAHANDI THUAMUL RAMPUR SINDHIPADAR SIRIMASKA 100.000000 0.000000 0.000000 33.330000 66.670000 0.000000 0.000000 33.330000 0.000000 0.000000
THUAMUL RAMPUR SIMILIPADAR 100.000000 0.000000 0.000000 0.000000 20.000000 0.000000 0.000000 100.000000 0.000000 10.000000
PALIJHAR 100.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000
MOTACHUAN 100.000000 0.000000 0.000000 0.000000 6.670000 6.670000 0.000000 93.330000 0.000000 10.000000
KOSABARA 100.000000 0.000000 0.000000 21.880000 6.250000 50.000000 0.000000 96.880000 0.000000 6.250000
KENDUPADA 100.000000 0.000000 0.000000 0.000000 0.000000 8.700000 0.000000 91.300000 0.000000 8.700000
GHATIGUDA 100.000000 0.000000 0.000000 0.000000 0.000000 11.110000 0.000000 55.560000 0.000000 0.000000
SINDHIPADAR ZILLA GAON 100.000000 0.000000 0.000000 46.150000 38.460000 0.000000 0.000000 84.620000 53.850000 23.080000
NICHEMASKA 100.000000 0.000000 0.000000 100.000000 50.000000 0.000000 0.000000 50.000000 0.000000 0.000000
NAKRUNDI TARAPADAR 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 14.290000 85.710000 0.000000 14.290000
SINDHIPADAR KANDAJHAPI 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 37.500000 0.000000 12.500000
BENDAJHOLA 100.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000
ARAKHPURI 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000
ODRI TENTULIPADA 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000
PENGDHUSI 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000
KOKELPADAR 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000
DHOLPASS 100.000000 0.000000 0.000000 33.330000 9.520000 0.000000 0.000000 80.950000 0.000000 4.760000
KANDHAMAL BALIGUDA RUTUNGIA KADIGANDA 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
SINDHRIGAON NILIPADA 100.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
DARINGIBADI DARINGIBADI SIRIPANKA 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000
K. NUAGAON SIRTIGUDA GASUKIA 100.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
KEONJHAR JHUMPURA TUKUDIHA BARAHAPOSI 100.000000 0.000000 0.000000 20.000000 20.000000 0.000000 0.000000 0.000000 80.000000 0.000000
NISCHINTAPUR NISCHINTAPUR 100.000000 0.000000 0.000000 42.860000 57.140000 0.000000 0.000000 14.290000 28.570000 0.000000
BARIA BHUBANAPOSI 100.000000 0.000000 0.000000 0.000000 30.770000 0.000000 0.000000 38.460000 0.000000 0.000000
BADANEULI RATNAPOSI 100.000000 0.000000 0.000000 0.000000 33.330000 0.000000 0.000000 33.330000 33.330000 33.330000
PODASIMILA 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 66.670000 66.670000
HARICHANDANPUR MANAHARAPUR SATYAPAL 100.000000 0.000000 0.000000 0.000000 0.000000 50.000000 0.000000 0.000000 0.000000 0.000000
JEERANGA TANGARPADA 100.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
SANTOSHPUR 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000
CHAMPUA SUNAPOSHI JADAPOKHARI 100.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000
SADANGI RAJABANDHA 100.000000 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000 39.470000 0.000000 0.000000
RANGAMATIA BANKIA 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000
PADUA BALIPOSHI 100.000000 0.000000 0.000000 25.000000 0.000000 0.000000 66.670000 8.330000 0.000000 16.670000
BHANDA GULUDIPOSHI 100.000000 0.000000 0.000000 0.000000 33.330000 16.670000 0.000000 25.000000 75.000000 0.000000
KANDHAMAL K. NUAGAON SIRTIGUDA KUDUPAKIA 100.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000 50.000000 0.000000
GUNJIGAON 100.000000 0.000000 0.000000 0.000000 0.000000 25.000000 0.000000 0.000000 0.000000 50.000000
KALAHANDI THUAMUL RAMPUR ODRI DALGUDA 100.000000 0.000000 0.000000 5.770000 0.000000 0.000000 0.000000 96.150000 0.000000 0.000000
NAKRUNDI TALAMPADAR 100.000000 0.000000 0.000000 90.910000 0.000000 0.000000 0.000000 100.000000 0.000000 100.000000
KEONJHAR KEONJHAR GOBARDHAN BANUAMAHANTA SAHI 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000 0.000000
KALAHANDI THUAMUL RAMPUR GUNUPUR NUNRESH 100.000000 0.000000 0.000000 40.000000 10.000000 0.000000 0.000000 0.000000 50.000000 40.000000
KANIGUMA CHARCHIKINA 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 41.180000 41.180000
BALABHADRA COLONY 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 94.440000 0.000000 97.220000
GUNUPUR TIKIRAPADA 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
SIMELPADAR-1 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 16.220000 18.920000
SIALIPADAR 100.000000 0.000000 0.000000 6.670000 3.330000 0.000000 0.000000 93.330000 0.000000 3.330000
SAISUMI 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 50.000000
RANAPUR 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
MUNDAPARA 100.000000 0.000000 0.000000 60.000000 40.000000 0.000000 0.000000 40.000000 0.000000 0.000000
NAKRUNDI SIGNI 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000

Frequency Distribution of Villages by reason for households(%) not being connected to Water Supply System there

Out[117]:
<matplotlib.axes._subplots.AxesSubplot at 0x15efc4ce2b0>

Why given % of households have not been connected to the water supply systsme in the village?

Out[118]:
(-0.001, 5.0] (5.0, 10.0] (10.0, 20.0] (20.0, 30.0] (30.0, 40.0] (40.0, 50.0] (50.0, 60.0] (60.0, 70.0] (70.0, 80.0] (80.0, 90.0] (90.0, 100.0]
Reason(s) for no water supply-Problem with the distribution pipeline-Binned 179 4 14 14 14 8 3 8 5 10 51
Reason(s) for no water supply-Other-Binned 253 3 7 0 3 3 5 6 5 5 20
Reason(s) for no water supply-Problem with the individual pipeline-Binned 211 2 14 13 9 17 4 5 4 3 28
Reason(s) for no water supply-Non-payment of user fee-Binned 221 6 9 7 5 17 3 4 5 3 30
Reason(s) for no water supply-Water shortage-Binned 205 14 13 9 13 15 5 7 6 3 20
Reason(s) for no water supply-Elevation issue-Binned 250 9 13 7 8 6 2 3 1 1 10
Reason(s) for no water supply-Low/No pressure in the individual pipeline-Binned 258 6 9 6 9 3 1 2 5 3 8
Reason(s) for no water supply-Committee decided-Binned 294 1 0 1 3 4 1 2 0 0 4
Reason(s) for no water supply-Caste/ Social Issue-Binned 304 1 1 1 0 1 0 0 0 0 2

4. Availability of 3rd Tap Connection in Households

Households having 3rd Tap Connection


Households which opted for TBR and/or 3rd Tap Connection

Out[120]:
Is there a third tap? Yes No
Do you have TBR?
No 923 7940
Toilet only 345 1264
Yes, I have TBR 13299 17794

Households which opted for Water Supply Connection and/or 3rd Tap Connection

Out[122]:
Is there a third tap? Yes No
Do you NOW have tap connection (individual pipeline) to your house/ TBR?
Yes 13726 17290
No 841 9708

What is the Condition of the 3rd Tap installed in Households?


Where have people installed the 3rd Tap preferably?


5. Sources of Drinking Water

Where do people get drinking water from?

% of Households reporting different sources of drinking water


Overall


By Connection to Water Supply


By Availability of 3rd Tap


*By District

In [129]:
hh_drnkng_wtr_src = raw_data.pivot_table(index='District',
               values = value,
               aggfunc=np.mean).round(2)
display_bar(hh_drnkng_wtr_src,size=[16,8],title='')

create_download_link(df=villages_with_no_supply_rsn,title="Download Complete List",
                     filename='List of District-Major Sources of Water for Households.csv',
                    level='District')

By Village


5. Disposal of Waste Water


How is waste water from bathroom disposed?

Number of households actually having waste water disposal system

22814  ( 54.89 % )

Where is waste water from the bathroom disposed?

Is the water water flowing out of bathroom properly disposed in a sewer/kitchen/garden/pit?

Out[134]:
Where is waste water from the bathroom disposed? Flows into a common system Kitchen Garden/ Plantation Not to any particular point Other (please specify) Soak pit
How is waste water from bathroom disposed?
No specific provision has been made 1116 319 5734 10 183
Other (please specify) 6 17 37 1274 4
Through a concrete dug channel 1731 1067 344 0 241
Through a piped channel 7947 2674 4841 16 2539

6. Usage of Toilet in the households

Does anyone in family use the toilet?

% of households where toilet is used by any of the family member


By Availability of TBR


Why does the family not use the toilet

Reason for not using the toilet (by % of Households)


a. Overall


b. District Wise


c. Village Wise


Although the households do not use toilet primarily due to lack of water, but what is the status of water supply in such households which are connected with water supply


In [143]:
raw_data[raw_data['Why dont they use the Toilet? (1)-No water']==100]['How often do you get water?'].value_counts()
Out[143]:
365 days a year but not 24/7    506
Not 12 months but 24/7          121
Not 12 months and not 24/7       77
365 days a year and 24/7         54
Name: How often do you get water?, dtype: int64
In [144]:
hh_tlt_no_usg_by_ws = raw_data[raw_data[par_col]=="No"].pivot_table(index=['How often do you get water?'],
               values = value,
               aggfunc=np.mean).round(2)
hh_tlt_no_usg_by_ws
Out[144]:
Why dont they use the Toilet? (1)-Habit Why dont they use the Toilet? (1)-Latrine overflow Why dont they use the Toilet? (1)-No electricity Why dont they use the Toilet? (1)-No toilets at work/ field Why dont they use the Toilet? (1)-No water Why dont they use the Toilet? (1)-Other (please specify) Why dont they use the Toilet? (1)-Prefer Defecating in Open Why dont they use the Toilet? (1)-Toilet too far Why dont they use the Toilet? (1)-Unable to use - broken Why dont they use the Toilet? (1)-Used only during emergencies
How often do you get water?
365 days a year and 24/7 0.66 8.80 0.13 8.28 7.10 0 13.67 4.86 26.15 13.01
365 days a year but not 24/7 0.36 8.88 0.47 4.67 26.27 0 19.63 4.52 17.96 8.00
Not 12 months and not 24/7 1.09 2.19 0.55 4.92 42.08 0 7.10 3.28 8.74 2.19
Not 12 months but 24/7 1.19 7.58 1.19 1.78 17.98 0 14.56 7.28 46.06 2.67

On which occasions do the households not use the toilet?

a. Overall

In [145]:
col="What are the occasions family members not use the Toilet?"
options=get_options(col)
decompose_multiselect_answers_normalized(col,options)
value=[col+"-"+x for x in options]
hh_tlt_no_use_occ = raw_data.pivot_table(index='State',
                                        values=value,
                                        aggfunc=np.sum)
display_bar(hh_tlt_no_use_occ,title='',size=[16,9],pct=True)

b. In Districts

In [146]:
hh_tlt_no_use_occ_dw = raw_data.pivot_table(index='District',
                                        values=value,
                                        aggfunc=np.sum)
display_bar(hh_tlt_no_use_occ_dw,title='',size=[16,9],pct=False)

7. Usage of Bathroom in the households

Does anyone in family use the bathroom?

% of households where bathroom is used by any of the family member


By Availability of TBR


Why does the family not use the bathroom

Reason for not using the bathroom (by % of Households)


a. Overall


b. District Wise


c. Village Wise


Although the households do not use bathroom majorly due to lack of water, but what is the status of water supply in such households


In [155]:
raw_data[raw_data['Why do they not use the bathroom?-No water']==100]['How often do you get water?'].value_counts()
Out[155]:
365 days a year but not 24/7    822
Not 12 months but 24/7          155
Not 12 months and not 24/7      114
365 days a year and 24/7         66
Name: How often do you get water?, dtype: int64
In [156]:
hh_bth_no_usg_by_ws = raw_data[raw_data[par_col]=="No"].pivot_table(index=['How often do you get water?'],
               values = value,
               aggfunc=np.mean).round(2)
hh_bth_no_usg_by_ws
Out[156]:
Why do they not use the bathroom?-Bathroom is cold Why do they not use the bathroom?-No water Why do they not use the bathroom?-Other (please specify) Why do they not use the bathroom?-Prefer bathing in the open
How often do you get water?
365 days a year and 24/7 3.29 10.86 0 31.41
365 days a year but not 24/7 1.26 37.03 0 27.70
Not 12 months and not 24/7 4.57 52.05 0 10.05
Not 12 months but 24/7 4.58 30.88 0 30.68

8. Usage of Toilet and Bathroom among Family Members of Different Age Groups **


Summary of Responses on TBR Usage in households, across different groups ad gender

In [161]:
hh_t_b_usg
Out[161]:
Usage Yes No Sometimes Total Yes% No% Sometimes%
0 Does anyone in family use the toilet? 25088 7613 0 32701 76.72 23.28 0.00
1 Does anyone in family use the Bathroom? 23627 7465 0 31092 75.99 24.01 0.00
2 How often do adult male members (18-60 Years) ... 22024 63 1179 23266 94.66 0.27 5.07
3 How often do adult male members (18-60 Years) ... 20831 118 1010 21959 94.86 0.54 4.60
4 How often do adult female members (18-60 Years... 22376 37 1089 23502 95.21 0.16 4.63
5 How often do adult female members (18-60 Years... 21173 74 933 22180 95.46 0.33 4.21
6 How often do elder male members (60+ Years) us... 5619 34 259 5912 95.04 0.58 4.38
7 How often do elder male members (60+ Years) us... 5276 47 254 5577 94.60 0.84 4.55
8 How often do elder female members (60+ Years) ... 5427 31 244 5702 95.18 0.54 4.28
9 How often do elder female members (60+ Years) ... 5116 35 200 5351 95.61 0.65 3.74
10 How often do boys (8-17 Years) use the toilet? 8690 23 437 9150 94.97 0.25 4.78
11 How often do boys (8-17 Years) use the Bathroom? 8267 55 393 8715 94.86 0.63 4.51
12 How often do girls (8-17 Years) use the toilet? 7929 17 369 8315 95.36 0.20 4.44
13 How often do girls (8-17 Years) use the Bathroom? 7576 27 334 7937 95.45 0.34 4.21

TBR Usage in households, across different groups ad gender

'----------------------------------------------------------------------------------------------'
"Why don't adult male members (18-60 Years) use the Toilet?"
'----------------------------------------------------------------------------------------------'
"Why don't adult male members (18-60 Years) use the bathroom?"
'----------------------------------------------------------------------------------------------'
"Why don't adult female members (18-60 Years) use the Toilet?"
'----------------------------------------------------------------------------------------------'
"Why don't adult female members (18-60 Years) use the bathroom?"
'----------------------------------------------------------------------------------------------'
"Why don't elder male members (60+ Years) use the Toilet?"
'----------------------------------------------------------------------------------------------'
"Why don't elder male members (60+ Years) use the bathroom?"
'----------------------------------------------------------------------------------------------'
"Why don't elder female members (60+ Years) use the Toilet?"
'----------------------------------------------------------------------------------------------'
"Why don't elder female members (60+ Years) use the bathroom?"
'----------------------------------------------------------------------------------------------'
'Why dont boys (8-17 Years) use the Toilet?'
'----------------------------------------------------------------------------------------------'
"Why don't boys (8-17 Years) use the bathroom?"
'----------------------------------------------------------------------------------------------'
"Why don't girls (8-17 Years) use the Toilet?"
'----------------------------------------------------------------------------------------------'
"Why don't girls (8-17 Years) use the bathroom?"

Section 3. Downloads

Status of Water Supply and TBR in Villages

In [169]:
hh_wss_tbr_staus = raw_data.pivot_table(index=['District','Block','Gram Panchayat', 'Village'],
                               values = ['Total Households',
                                         "Caste (Avoiding asking. Ask only if doubtful)-SC",
                                         "Caste (Avoiding asking. Ask only if doubtful)-ST",
                                         "Caste (Avoiding asking. Ask only if doubtful)-OBC",
                                         "Caste (Avoiding asking. Ask only if doubtful)-General",
                                         'Do you have TBR?-Yes, I have TBR',
                                         'Do you have TBR?-Toilet only',
                                         'Do you have TBR?-No',
                                         'Did you ever have TBR?-No',
                                         'Do you NOW have tap connection (individual pipeline) to your house/ TBR?-Yes',
                                         'Do you NOW have tap connection (individual pipeline) to your house/ TBR?-No',
                                         'Do you have water supply to your house/TBR ?-Yes',
                                         'Do you have water supply to your house/TBR ?-No'],
                               aggfunc=np.sum)
Out[170]:

Household Survey Overview

In [183]:
create_download_link(hh_wss_status,title='Click to Download', filename='Household Survey Overview.csv',level='Village')
Out[183]: